RQ00245 - Machine Learning Engineer - Senior

Flexstaf IT; Toronto; Hybrid; CAD 120,000 - 170,000; Full time

Job summary

Flexstaf IT in Canada’s Ontario region seeks a Senior Machine Learning Engineer to deploy, monitor, and operationalize AI solutions using modern MLOps.

You will establish reusable deployment patterns, tooling, and observability, enabling models to move reliably from development to production at scale across the enterprise.

The role requires deep ML expertise, strong Python skills, and experience with Azure ML, Databricks, CI/CD, data pipelines, and performance tuning.

Qualifications

• Advanced hands-on experience with Azure ML, Foundry/Azure AI Foundry, MLOps, CI/CD, containers, APIs, automation, model deployment, monitoring and observability.
• Strong Python and machine-learning engineering skills with demonstrated experience taking models from experimentation into reliable production environments. Tech stack includes Python, SQL, Power BI, Power Automate, VSCode, Git, Jira, Azure ML Studio, Databricks.
• Deep understanding of ML algorithms and statistics, including classification, regression, clustering, feature engineering, model evaluation, hyperparameter tuning, drift, retraining, performance optimization and production model troubleshooting.

Responsibilities

• Deploy, integrate, monitor, and operationalize AI and ML solutions using modern MLOps practices.
• Establish reusable deployment patterns, tooling, monitoring, and automation that enable AI solutions to move reliably from development into production and scale across the enterprise.
• Designs and develops machine learning and deep learning systems; runs machine learning tests and experiments.

Skills

Python

ML engineering

MLOps

CI/CD

Containers

APIs

Automation

Model deployment

Monitoring

SQL

Tools

Azure ML Studio

Azure Foundry

Databricks

Power BI

Power Automate

VSCode

Git

Jira

Job description

Must Haves

• Advanced hands-on experience with Azure Machine Learning, Microsoft Foundry/Azure AI Foundry, MLOps, CI/CD, containers, APIs, automation, model deployment, monitoring and observability.
• Strong Python and machine-learning engineering skills with demonstrated experience taking models from experimentation into reliable production environments. Tech stack: Python, SQL, Power BI, Power Automate, VSCode, Git, Jira, Azure (ML Studio, AI Foundry), Databricks.
• Deep understanding of ML algorithms and statistics, including classification, regression, clustering, feature engineering, model evaluation, hyperparameter tuning, drift, retraining, performance optimization and production model troubleshooting.

Location

Hybrid - 3 days a week onsite

Public Sector Experience

preferred but not required

Job Title

Machine Learning Engineer - Senior

Description

The Senior Machine Learning Engineer will deploy, integrate, monitor, and operationalize AI and machine-learning solutions using modern MLOps practices. The role will establish reusable deployment patterns, tooling, monitoring, and automation that enable AI solutions to move reliably from development into production and scale across the enterprise. Responsibilities: Creates machine learning models and utilizes data to train models Focuses on analyzing data to find relations between the input and the desired output Understands business objectives and develops models that help achieve them, along with metrics to track their progress Designs and develops machine learning and deep learning systems Runs machine learning tests and experiments Implements appropriate machine learning algorithms General Skills: Experience managing available resources such as hardware, data, and personnel so that deadlines are met Experience analyzing the machine learning algorithms that could be used to solve a given problem and ranking them by their success probability Experience exploring and visualizing data to gain an understanding of it, then identifying differences in data distribution that could affect performance when deploying the model in the real world Experience verifying data quality, and/or ensuring it via data cleaning Experience supervising the data acquisition process if more data is needed Experience finding available datasets online that could be used for training Experience defining validation strategies Experience defining the preprocessing or feature engineering to be done on a given dataset Background in statistics and computer programming A team player with a track record for meeting deadlines, managing competing priorities and client relationship management experience